Of the 182 identified genera of importance for deer and elk forage or sage-grouse, we had sufficient data (e.g. at least 3 ecognition polygons) to train models for 153 of the genera
In the vast majority of cases, Random Forest models appeared to train the best predictive models based on AUC values.
| BestModel | Number of Genera |
|---|---|
| RF | 89 |
| XGBOOST | 26 |
| GBM | 12 |
| GLMNET | 12 |
| GLM | 7 |
| GLMNET_class | 6 |
| RF,XGBOOST | 1 |
We were able to attain AUC values of >= 0.8 for 76% of the trained genera models.
AUC values plotted according to sample size
Data summary of trained models.
Of the 593 identified species of importance for deer and elk forage or sage-grouse, we had sufficient data (e.g. at least 3 ecognition polygons) to train models for 240 of the species.
In the vast majority of cases, Random Forest models appeared to train the best predictive models based on AUC values.
| BestModel | Number of Species |
|---|---|
| RF | 111 |
| XGBOOST | 51 |
| GLMNET | 32 |
| GBM | 19 |
| GLMNET_class | 17 |
| GLM | 8 |
| GLM,RF | 1 |
| RF,XGBOOST | 1 |
We were able to attain AUC values of >= 0.8 for 85% of the trained species models.
AUC values plotted according to sample size
Data summary of trained models.
Of the 216 identified subspecies of importance for deer and elk forage or sage-grouse, we had sufficient data (e.g. at least 3 ecognition polygons) to train models for 9 of the subspecies.
| BestModel | Number of Subspecies |
|---|---|
| RF | 4 |
| GBM | 2 |
| XGBOOST | 2 |
| GLMNET | 1 |
We were able to attain AUC values of >= 0.8 for 78% of the trained subspecies models.
AUC values plotted according to sample size
Data summary of trained models.